Representation

Why the World Needs More Women Data Scientists

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Author: Ugonma Nwankwo, Michael Pisa; Publisher: Center for Global Development; Publication Year: 2021. The following article discusses how evidence-based policy making is ineffective when it relies on biased information, a potential source for this is bias in the datasets. In the U.S., women make up 18% of data scientist jobs and in lower-income countries, that stat is even worse. In the data value chain, which includes collection, publication, uptake and…

Incorporate Inclusivity – Data Science Ethics Podcast

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Author: Marie Weber, Lexy Kassan; Publisher: Data Science Ethics; Publication Year: 2020. The following podcast episode discusses making sure that minorities are represented in the process of both building and testing especially if the results will be impacting those groups. They also suggested incorporating people from other departments across the business to bring in new and alternative ideas and test your work against…

Crunching the Numbers on Diversity in Data Science: Events & Resources to Foster Inclusion

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Author: Laramie Paxton; Publisher: Medium; Publication Year: 2020. The following article describes how data science is currently one of the most attractive jobs due to job availability and competitive salaries, but it has a diversity problem. Only 15% of data scientists are women and as data science professionals advance in their careers, the number of women gets lower. Similar trends can be seen for other…

8 Types of Data Bias That Can Wreck Your Machine Learning Models

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Author: Joanna Kaminska; Publisher: Statice; Publication Year: 2022. The following article discusses how it is important to know that while working with data there is a possibility that the data is biased; this is a big problem due to the fact that if this biased data is used to create an machine learning (ML) model, then the model too will most likely be a biased model. The problem with a biased model is that discrimination…

Health Data Poverty: An Assailable Barrier to Equitable Digital Health Care

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Author: Hussein Ibrahim, Xiaoxuan Liu, Nevine Zariffa, Andrew D. Morris, Alastair K. Denniston; Publisher: The Lancet Digital Health; Publication Year: 2021. The following study identifies how machine learning and advanced technologies can now be used to transform health data, but such innovations could widen the gap of healthcare inequalities for those who may suffer from health data poverty. Health data, or health-related information about a person, can be used to make diagnoses or improve…

Truth in Pictures: What Google Image Searches Tell Us About Inequality at Work

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Author: Gretchen Hellman; Publisher: Diversity Employers; Publication Year: 2017. The following article speaks about the bias of search engines. If you were to look for images of a “professor” or “CEO” you would find images of older white males. If you looked for images of a “Nurse” or “Teacher” you would get images of younger females. Given that most CEOs are white males and only 4% of Fortune 500 CEOs are women, it is easy to…

The Impact of AI on Society

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Author: N/A; Publisher: Data Ethics 4 All; Publication Year: N/A. The following panel discusses ethical artificial intelligence (AI) practices, and how the data science community needs to balance human values with the values of an efficient algorithm. These values include: representation, fairness, lessons learned, evidence based governing procedures, progress, understanding, and transparency. The panel also discussed…

Trust in the Digital Age Requires Data Ethics and a Data Literate Workforce

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Author: Andrew Beers; Publisher: Forbes; Publication Year: 2022. The following article lists guidelines for ethical standards for companies using data ethics and AI. The first of the 3 guidelines is by setting up review panels. These review panels are independent organizations that can provide oversight, training, and other standards of practice. The second guideline is using resources that…